Tag Archives: Regulation

Two weeks ago I was on a three person half hour panel on “Bitcoin and the Future” at an O’Reilly Radar Summit on Bitcoin & the Blockchain. I was honored to be invited, but worried as I had not been tracking the field much. I read up a bit, and listened carefully to previous sessions. And I’ve been continuing to ponder and read for the last two weeks. There are many technical details here, and they matter. Even so, it seems I should try to say something; here goes.

A possible conversation between a blockchain enthusiast and newbie:

“Bitcoin is electronic money! It is made from blockchains, which are electronic ledgers that can also support many kinds of electronic contracts and trades.”

“But we already have money, and ledgers. And electronic versions. In fact, bank ledgers were one of the first computer applications.”

“Yes, but blockchain ledgers are decentralized. Sure, compared to ordinary computer ledgers, blockchain ledgers take millions or more times the computing power. But blockchains have no central org to trust. Instead, you trust the whole system.”

“Is this whole system in fact more more trustworthy that the usual bank ledger system today?”

“Not in practice so far, at least not for most people. But it might be in the future, if we experiment with enough different approaches, and if enough people use the better approaches, to induce enough supporting infrastructure efforts.”

“Yes, but credit card firms charge you way too much for such services.”

“And without central orgs, doesn’t it get much harder to regulate financial services?”

“Yes, but you don’t want all those regulations. For example, blockchains make anonymous money holdings and contracts easier. So you could evade taxes, and laws that restrict bets and drug buys.”

“Couldn’t we just pass new laws to allow such evasions, if we didn’t want the social protections they provide? And couldn’t we just buy cheaper financial services, if we didn’t want the private protections that standard services now provide?”

“You’re talking as if government and financial service markets are efficient. Theyaren’t. Financial firms have a chokehold on finance, and they squeeze us for their gain, not ours. They have captured government regulators, who mostly work to tighten the noose, instead of helping the rest of us.”

“OK, imagine we do create cheaper decentralized systems of finance where evasion of regulation is easier. If this system is used in ways we don’t like, we won’t be able to do much to stop that besides informal social pressure, or trying to crudely shut down the whole system, right? There’d be no one driving the train.”

“Yes, exactly! That is the dream, and it might just be possible, if enough of us work for it.”

“But even if I want change, shouldn’t I be scared of change this lumpy? This is all or nothing. We don’t get to see the all before we try, and once we get it then its mostly too late to reverse.”

“Yes, but the powers-that-be can and do block most incremental changes. It is disruptive revolution, or nothing. To the barricades!”

I see five main issues regarding blockchain enthusiasm:

Technical Obstacles. Many technical obstacles remain, to designing systems that are general, cheap, secure, robust, and scaleable. You are more enthusiastic if you think these obstacles can be more easily overcome.

Bad Finance & Regulation. The more corrupt and wasteful you think that finance and financial regulation are today, the more you’ll want to throw the dice to get something new.

Lumpy Change. The more you want change, but would rather go slow and gradual, so we can back off if we don’t like what we see, the less you’ll want to throw these lumpy dice.

Standards Coordination. Many equilibria are possible here, depending on exactly which technical features are in the main standards. The worse you think we are at such coordination, the less you want to roll these dice.

Risk Aversion. The more you think regulations protect us from terrible dark demons waiting in the shadows, the less you’ll want a big unknown hard-to-change-or-regulate world.

Me, I’d throw the dice. But then I’d really like more bets to be feasible, and I’ve known some people working in this area for decades. However, I can’t at all see blaming you if you feel different; this really is a tough call.

Libertarians focus too much on trying to argue abstractly that liberty would be better, and not enough on just concretely describing how liberty would be different. … From [our] vast literature we should be able to identify many concrete patterns and “stylized facts” about how government-provision and heavy-regulation tends to change products and services. (more)

David Henderson agrees:

The reality is that after Stigler’s speech, many economists did look more at the data and the data tended to show that the free market and economic freedom work better than government control. But Robin is not satisfied. There is more to be done, he says, and he’s right. (more)

But he does have a criticism:

I do have one main criticism of Robin’s post. … It’s the West/East Germany and the South/North Korea comparisons that I want to defend. With all the variables that could affect economic growth, think about how hard it is to know what some of the most important factors are. … The stark contrast between those two pairs of countries and what that said about some economic freedom versus harsh totalitarianism.

I very much agree that those nation pairs make useful comparisons; sorry that what I wrote could mislead on that point. These comparisons do indeed suggest that “some freedom” is better than “harsh totalitarianism”, and they are good data-points on which to base stylized facts on the general effects of more liberty. Their main limitations are that they don’t say much directly about the effects of a lot more liberty than is found in West Germany or South Korea. To imagine even more liberty, we need those stylized facts.

What is our main problem, bad policy or bad meta-policy? That is, do our collective choices go wrong mainly because we make a few key mistakes in choosing particular policies? Or do they go wrong mainly because we use the wrong institutions to choose these policies?

I would have thought meta-policy was the obvious answer. But CATO asked 51 scholars/pundits this question:

If you could wave a magic wand and make one or two policy or institutional changes to brighten the U.S. economy’s long-term growth prospects, what would you change and why?

And out of the 29 answers now visible, only four (or 14%) of us picked meta-policy changes:

BLS data on gross labor market flows … are not available at the state and MSA level, they do not have detailed industry breakdowns, and they do not break down by occupation or by job task. … We also need better “longitudinal” data — data that track individuals every year (or even more frequently) for a long period of time. … The major federal statistical agencies need larger budgets to collect the data we need to design policies to increase workforce participation and to strength future growth. … My second policy suggestion is to expand the … EITC.

I would triple the amount the Congress spends on staff (keeping it still at just under 0.1% of the total federal budget). I’d also concentrate that spending in the policy committees. I’d give those committees the resources to be leading institutions for expertise on the issues on which they deal. I’d also give these committees the resources to hire their own experts — economists, lawyers, consultants, etc. But I’d also make sure that these committees were not explicitly partisan.

A performance bonus would help to overcome some of Congress’s complacency and division in the face of decades-long economic stagnation. … One good performance metric would be total factor productivity (TFP). … Fernald adjusts his TFP estimate for cyclical labor and capital utilization changes, making his series a better measure. … Members of Congress would earn a $200,000 bonus if the two-year period in which they serve averages 2 percent TFP growth. (more)

First, I propose that our national legislatures pass bills to define national welfare, and fund and authorize an agency to collect statistics to measure this numerical quantity after the fact. … Second, … create an open bounty system for proposing policies to increase national welfare. … Third, … create two open speculative decision markets for each official proposal, to estimate national welfare given that we do or do not adopt this proposal. … If over the decision day the average if-adopted price is higher than the average if-not-adopt price (plus average bid-ask spread), then the proposal … becomes a new law of the land.

It seems to me that Michael, Lee, and Eli feel wave pretty weak wands. Surely if they thought their wands strong enough to cast any policy or meta-policy spell, wouldn’t they pick meta-policy spells a bit stronger than these? (And why is it always more spending, not less?)

By focusing on policy instead of meta-policy, it seems to me that the other 25 writers show either an unjustified faith in existing policy institutions, or a lack of imagination on possible alternatives. Both of which are somewhat surprising for 51 scholars chosen by CATO.

Added Dec3: 3 of the 25 remaining proposals were in the meta-policy direction:

[Regulatory] agencies should be required to present evidence that they have identified a material failure of competitive markets or public institutions that requires a federal regulatory solution, and provide an objective evaluation of alternatives.

The Regulatory Improvement Commission … would have a limited period of time to come up with a package of regulations to be eliminated or fixed, drawing on public suggestions. The package would then be sent to Congress for an up-or-down vote, and then onto the President for signing.

Instead of analyzing whether the [cost-benefit] calculations in a regulatory ledger sum to a positive or a negative number, we need to set a level of [regulatory] complexity that we’re willing to live with, and then decide which positive sum regulations we’re willing to discard in order to stay within that budget. … Crude rules which might well serve, like capping the number of laws and regulations, allowing a new one to be implemented only if an older one is repealed.

Added 30Sept2015: There are now 51 of these proposals, collected into a book. I found no more that are plausibly meta-proposals.

In this post I’ll talk primarily to people who, like me, lean libertarian. The rest of you can take a break.

Libertarians want to move more products and services from being provided directly by government, to being provided privately. And for those that are provided privately, libertarians want to weaken regulations. These changes would increase liberty.

Libertarians tend to offer arguments that are relatively abstract and theory-based. That is, they focus more on why more liberty is more moral, or why it should in theory give better outcomes. They focus less on showing that liberty has in practice worked out better. When libertarians do focus on data, they tend to be very broad, or randomly specific. That is, they talk about how West Germany is better than East Germany, or South Korea better than North Korea. Or they pick on very specific examples, like regulations limiting eyeglass ads, and leave audiences wondering how cherry-picked are such examples.

It seems to me that libertarians focus too much on trying to argue abstractly that liberty would be better, and not enough on just concretely describing how liberty would be different. Yes for you the abstract arguments seem best; they persuade you plenty, and they bring the most prestige in your circle. But typical libertarians today are a distinct personality type; most people are not like you. Most people just cannot be comfortable with a proposal for change if they cannot imagine it in some detail, and imagine that they’d like that detail. Such people don’t need more abstract arguments and examples; they instead credible concrete descriptions.

True, people have sometimes written fiction set in libertarian settings. But such fiction doesn’t usually come with a careful analysis of why one should believe in its many details. Yes, part of the attraction of liberty is that it frees up people to innovate in ways that one can’t anticipate in advance. But that doesn’t mean that we can’t go a long way to better describe a world of more liberty.

On reflection, I realize that when I try to imagine more liberty, I mostly draw on a limited set of iconic comparisons, such as comparing airlines, trucks, and phones before and after US deregulation, or comparing public to private schools and mail in the US. Alas, we and our audiences should worry that we cherry-pick such examples to support conclusions we like.

We should be able to do much better than this. By now there are vast literatures discussing many industries in many places before and after regulation or deregulation, and describing specific times and places where certain products and services provided directly by governments, or provided privately. From this vast literature we should be able to identify many concrete patterns and “stylized facts” about how government-provision and heavy-regulation tends to change products and services.

I recall these suggestions for typical features of industries with more liberty:

Less “gold-plating” in materials and methods

More product variety, including more low quality versions

Faster innovation and product cycles

Fewer guarantees to workers or customers

Price, features vary more with customer features

Workers have less school and seniority

Less overhead spend on paperwork

more?

Some people should work to extract patterns like these from our vast related literatures – I’ve looked, and there just aren’t many such summaries today. With such patterns in hand, we would be in a much better position to credibly describe how familiar products and services would concretely change if we were to provide them privately, or to regulate them less. And such credible concrete descriptions might allow many more people to become comfortable with endorsing such expansions of liberty.

This sort of project seems well within the abilities of the median grad student. It doesn’t require great creativity or technical skills. Instead, it just requires methodically surveying and summarizing related literatures. Perhaps some libertarian students should shy away from it in hopes of impressing via more difficult methods. But surely there must be other students for which this sort of project is a good match.

We often praise and criticize people for the things they do. And while we have many kinds of praise, one very common type (which I focus on in this post) seems to send the message “what you did was good, and it would be good if more of that sort of thing were done.” (Substitute “bad” for “good” to get the matching critical message.)

Now if it would be good to have more of some act, then that act is a good candidate for something to subsidize more. And if most people agreed that this sort of act deserved more subsidy, then politicians should be tempted to run for office on the platform that they will increase the actual subsidy given to that kind of act. After all, if we want more of some kind of acts, why don’t we try to better reward those acts? And so good acts shouldn’t long remain with an insufficient subsidy. Or bad acts without an insufficient tax.

But in fact we seem to have big categories of acts which we consistently praise for being good, and where this situation persists for decades or centuries. Think charity, innovation, or artistic or sport achievement. Our political systems do not generate much political pressure to increase the subsidies for such things. Subsidy-increasing proposals are not even common issues in elections. Similarly, large categories of acts are consistently criticized, yet few politicians run on platforms proposing to increase taxes on such acts.

My best interpretation of this situation is that while our words of praise give the impression that we think that most people would agree that the acts we praise are good, and should be more common, we don’t really believe this. Either we think that the acts signal impressive or praise-worthy features, but shouldn’t be more common, or we think such acts should be more common, but we also see large opposing political coalitions who disagree with our assessment.

That is, my best guess is that when we look like we are praising acts for promoting a commonly accepted good, we are usually really praising impressiveness, or we are joining in a partisan battle on what should be seen as good.

Because my explanation is cynical, many people count it as “extraordinary”, and think powerful extraordinary evidence must be mustered before one can reasonably suggest that it is plausible. In contrast, the usual self-serving idealistic explanations people give for their behavior are ordinary, and therefore can be accepted on face value without much evidence at all being offered in their defense. People get mad at me for even suggesting cynical theories in short blog posts, where large masses of extraordinary evidences have not been mustered. I greatly disagree with this common stacking of the deck against cynical theories.

As a professor of economics in the GMU Center for the Study of Public Choice, I and my colleagues are well aware of the many long detailed disputes on the proper scope of regulation.

One the one hand, the last few centuries has seen increasing demands for and expectations of government regulation. A wider range of things that might happen without regulation are seen as intolerable, and our increasing ability to manage large organizations and systems of surveillance is seen as making us increasingly capable of discerning relevant problems and managing regulatory solutions.

On the other hand, some don’t see many of the “problems” regulations are set up to address as legitimate ones for governments to tackle. And others see and fear regulatory overreach, wherein perhaps well-intentioned regulatory systems actually make most of us worse off, via capture, corruption, added costs, and slowed innovation.

The poster-children of regulatory overreach are 20th century totalitarian nations. Around 1900, many were told that the efficient scale of organization, coordination, and control was rapidly increasing, and nations who did not follow suit would be left behind. Many were also told that regulatory solutions were finally available for key problems of inequality and inefficient resource allocation. So many accepted and even encouraged their nations to create vast intrusive organizations and regulatory systems. These are now largely seen to have gone too far.

Or course there have no doubt been other cases of regulatory under-reach; I don’t presume to settle this debate here. In this post I instead want to introduce jaded students of regulatory debates to something a bit new under the sun, namely a newly-prominent rationale and goal for regulation that has recently arisen in a part of the futurist community: stopping preference change.

In history we have seen change not only in technology and environments, but also in habits, cultures, attitudes, and preferences. New generations often act not just like the same people thrust into new situations, but like new kinds of people with new attitudes and preferences. This has often intensified intergenerational conflicts; generations have argued not only about who should consume and control what, but also about which generational values should dominate.

So far, this sort of intergenerational value conflict has been limited due to the relatively mild value changes that have so far appeared within individual lifetimes. But at least two robust trends suggest the future will have more value change, and thus more conflict:

Longer lifespans – Holding other things constant, the longer people live the more generations will overlap at any one time, and the more different will be their values.

Faster change – Holding other things constant, a faster rate of economic and social change will likely induce values to change faster as people adapt to these social changes.

Value plasticity – It may become easier for our descendants to change their values, all else equal. This might be via stronger ads and schools, or direct brain rewiring. (This trend seems less robust.)

These trends robustly suggest that toward the end of their lives future folk will more often look with disapproval at the attitudes and behaviors of younger generations, even as these older generations have a smaller proportional influence on the world. There will be more “Get off my lawn! Damn kids got no respect.”

The futurists who most worry about this problem tend to assume a worst possible case. (Supporting quotes below.) That is, without a regulatory solution we face the prospect of quickly sharing the world with daemon spawn of titanic power who share almost none of our values. Not only might they not like our kind of music, they might not like music. They might not even be conscious. One standard example is that they might want only to fill the universe with paperclips, and rip us apart to make more paperclip materials. Futurists’ key argument: the space of possible values is vast, with most points far from us.

This increased intergenerational conflict is the new problem that tempts some futurists today to consider a new regulatory solution. And their preferred solution: a complete totalitarian takeover of the world, and maybe the universe, by a new super-intelligent computer.

You heard that right. Now to most of my social scientist colleagues, this will sound bonkers. But like totalitarian advocates of a century ago, these new futurists have a two-pronged argument. In addition to suggesting we’d be better off ruled by a super-intelligence, they say that a sudden takeover by such a computer will probably happen no matter what. So as long as we have to figure out how to control it, we might as well use it to solve the intergenerational conflict problem.

Now I’ve already discussed at some length why I don’t think a sudden (“foom”) takeover by a super intelligent computer is likely (see here, here, here). Nor do I think it obvious that value change will generically put us face-to-face with worst case daemon spawn. But I do grant that increasing lifespans and faster change are likely to result in more intergenerational conflict. And I can also believe that as we continue to learn just how strange the future could be, many will be disturbed enough to seek regulation to prevent value change.

Thus I accept that our literatures on regulation should be expanded to add one more entry, on the problem of intergenerational value conflict and related regulatory solutions. Some will want to regulate infinity, to prevent the values of our descendants from eventually drifting away from our values to parts unknown.

I’m much more interested here in identifying this issue than in solving it. But if you want my current opinion it is that today we are just not up to the level of coordination required to usefully control value changes across generations. And even if we were up to the task I’m not at all sure gains would be worth the quite substantial costs.

Added 8a: Some think I’m unfair to the fear-AI position to call AIs our descendants and to describe them in terms of lifespan, growth rates and value plasticity. But surely AIs being made of metal or made in factories aren’t directly what causes concern. I’ve tried to identify the relevant factors but if you think I’ve missed the key factors do tell me what I’ve missed.

Added 4p: To try to be even clearer, the standard worrisome foom scenario has a single AI that grows in power very rapidly and whose effective values drift rapidly away from ones that initially seemed friendly to humans. I see this as a combination of such AI descendants having faster growth rates and more value plasticity, which are two of the three key features I listed.

Economists are often stereotyped as claiming that firms are very economically efficient, i.e., that they very effectively minimize costs and maximize profits. This is a common source of derision of economists by other social scientists. And it is true that efficiency is the standard assumption made in textbooks and in math models. But over time I’ve been persuaded that it is often far from an accurate assumption. (And I doubt that most older economists believe it.)

I’ve been persuaded by a steady accumulation of plausible examples of widespread persistent inefficiencies. No one example is overwhelmingly obvious – all have stories for why they are only apparent inefficiencies. But added all together, they persuade me. Some examples:

Threats Help Productivity – When firms face more competition, they often have big bursts of productivity. But if increases were possible, why not do them before?

Long-Lasting Deadwood – Firms often keep employees who are widely known within the firm to not be pulling their weight relative to other employees. They tend to be fired during a downturn, or after a takeover.

Not Invented Here – Firms are famously reluctant to adopt changes that appear to have been developed elsewhere, preferring instead changes for which someone internal can take credit.

Shooting Messengers – Many firms greatly discourage passing bad news up to bosses. GM was just exposed as such a firm via a safety issue. Those who do pass bad news up are punished as if they were personally a big cause of the bad news.

Yes Men – If bosses keep quiet about their opinion, they can evaluate subordinates via comparing employee opinions with boss opinion. But bosses consistently forgo this by telling subordinates lots of opinions and punishing those who question such opinions.

Poison Pills – Rules that discourage takeover attempts by financially penalizing such attempts prevent investors from getting more for their shares.

Overpaid CEOs – It is far from clear that firms actually earn more when they hire more expensive CEOs.

Too Many Meetings – It is widely believed that most firms hold too many meetings that go on too long with too many people.

Too Many Interviews – It is hard to find much evidence that interviews add info on job performance. So why do candidates go through so many interviews?

Biased Evaluations – Bosses consistently give lower evaluations to people they didn’t hire, relative to people they did hire. Yet official evaluations don’t correct for this.

Excess Credentials – People consistently feel pressure to hire people whose credentials make them look good on paper, relative to people they believe would do a better job.

Few Experiments – Firms tend to be reluctant to do experiments, such as to find preferred product variations. Experiments would force them to admit they don’t yet know.

Few Track Records – Meetings are full of people making predictions on decision consequences, but firms almost never keep formal track records to rate accuracy.

Reward Braggarts – Firms consistently neglect people who don’t toot their own horn, even when their superior features are widely known.

Allow Info Silos – Groups and divisions with a firm are allowed to keep a lot of info secret within their group. Yet if the firm works together toward a common goal, what can be the benefit of keeping such secrets?

Predictable Consultants – Management consultants are often hired at great expense to give advice that is quite predictable given the opinions of those who hired them.

Cubicles – They seem to reduce productivity more than they save in office space costs.

I’ll add more here in response to suggestions.

My working hypothesis to explain these inefficiencies is that the people and supporting coalitions closest to them tend to gain from them, and that selection pressures on political coalitions are often much stronger than selection pressures on firms.

If many of these inefficiencies are real, then yes government regulators can also see them, and yes it might not be that hard for smart sincere people to design regulations to increase welfare by correcting for them. However, government regulatory agencies are also “inefficient” in many ways, leading them to choose and enforce regulations which differ from those that would most increase welfare. To judge if we are better off giving regulators more powers over firms, we must judge the relative magnitudes of these two types of inefficiencies.

Note that firm efficiency may still be a reasonable assumption to make in models, even if it is not an accurate assumption. Modeling is always a tradeoff between realism and understanding.

It is well-known that while electricity led to big gains in factory productivity, few gains were realized until factories were reorganized to take full advantage of the new possibilities which electric motors allowed. Similarly, computers didn’t create big productivity gains in offices until work flow and tasks were reorganized to take full advantage.

Auto autos, i.e., self-driving cars, seem similar: while there could be modest immediate gains from reducing accident rates and lost productive time commuting, the biggest gains should come from reorganizing our cities to match them. Self-driving cars could drive fast close together to increase road throughput, and be shared to eliminate the need for parking. This should allow for larger higher-density cities. For example, four times bigger cities could plausibly be twenty-five percent more productive.

But to achieve most of these gain, we must make new buildings with matching heights and locations. And this requires that self-driving cars make their appearance before we stop making so many new buildings. Let me explain.

Since buildings tend to last for many decades, one of the main reasons that cities have been adding many new buildings is that they have had more people who need buildings in which to live and work. But world population growth is slowing down, and may peak around 2055. It should peak earlier in rich nations, and later in poor nations.

Cities with stable or declining population build a lot fewer buildings; it would take them a lot longer to change city organization to take advantage of self-driving cars. So the main hope for rapidly achieving big gains would be in rapidly growing cities. What we need is for self-driving cars to become available and cheap enough in cities that are still growing fast enough, and which have legal and political support for driving such cars fast close together, so they can achieve high throughput. That is, people need to be sufficiently rewarded for using cars in ways that allow more road throughput. And then economic activity needs to move from old cities to the new more efficient cities.

This actually seems like a pretty challenging goal. China and India are making lots of buildings today, but those buildings are not well-matched to self-driving cars. Self-driving cars aren’t about to explode there, and by the time they are cheap the building boom may be over. Google announced its self-driving car program almost four years ago, and that hasn’t exactly sparked a tidal wave of change. Furthermore, even if self-driving cars arrive soon enough, city-region politics may well not be up to the task of coordinating to encourage such cars to drive fast close together. And national borders, regulation, etc. may not let larger economies be flexible enough to move much activity to the new cities who manage to support auto autos well.

Alas, overall it is hard to be very optimistic here. I have hopes, but only weak hopes.

Piketty’s big idea is that we are in the early stages of returning to a society dominated by great dynastic fortunes, by inherited wealth. … Imagine a wealthy family that has managed, somehow or other, to guarantee that a large fraction of its income is used to accumulate more wealth. Can this family thereby acquire a dominant position in society?

The answer depends on the relationship between r, the rate of return on assets, and g, the overall rate of economic growth. If r is less than g, dynasties are doomed to erode: even if all income from a very large fortune is devoted to accumulation, the family’s wealth will grow more slowly than the economy, and it will slowly slide into obscurity. But if r is greater than g, dynastic wealth can indeed grow to gigantic size. …

Piketty tells us something remarkable: historically, r has almost always exceeded g – but there was an exceptional period in the 20th century, a period of rapid labor force growth and technological progress, when r was less than g. And he asserts that the kind of society we consider normal, in which high incomes reflect personal achievement rather than inherited wealth, is in fact an aberration driven by this exceptional period. … A couple of questions:

1. How much of the decline in r relative to g in the 20th century reflected fast growth, and how much reflected policies that either taxed or in effect confiscated inherited wealth? In other words, how much was destiny, how much wars and political upheaval? Piketty stresses both factors, but never gives us a relative quantitative assessment. (more from Piketty here, here)

This rate of return on assets r that Krugman and Piketty discuss is something like the ratio of rental to purchase price of land. I don’t have access to Piketty’s book, but I’ve been pondering this question for a few months, and I’ve concluded that the usual estimates of asset returns r must fail to include many taxes that in practice reduce the actual rate of return r that growing dynasties can achieve. And I think that once we include all hidden taxes, the actual rate of return r that dynasties could achieve in practice must have usually be no more than the economic growth rate g. Let me explain.

Some taxes are explicit, like property taxes. Other taxes are implicit in the property destruction and transfer that result from wars, political upheavals, and legal corruption, and in the costs of reasonable efforts to prevent such losses. Finally, there are implicit taxes resulting from local legal limits on who one may use to manage a dynastic fund. For example, if a dynasty must give its eldest living male wide discretion over spending and investment choices, and if such males often turn out to be spent-thrift fools, this will greatly limit this dynasty’s ability to grow over the long run. An ideal might be to delegate dynasty management to a reputed professional trust that is legally obligated to follow explicit instructions to grow the fund as fast as possible over the long run. But, as I’ve discussed before, most societies have put substantial legal obstacles before solutions like this.

I argue that the net effect of all these hidden taxes on dynastic funds must have been to usually reduce asset returns to below growth rates. My argument is simple: If asset returns had typically been above growth rates, then if any dynastic funds had chosen to grow at the maximum possible rate, then even if those funds had started small they would have come to dominate investments worldwide. And they would have done so on a timescale short compared to the time period over which historical records suggest that asset returns have exceeded growth rates. By competing with each other, such dominating dynastic funds would then have increased the supply of investment so much as to drive down asset returns to or below the sustainable level, which is the economic growth rate.

I conclude that consistently across space and time, the net effects of all forms of taxes on dynastic investment funds, including taxes implicit in limiting who one may trust not to pilfer those funds, has been to reduce real assets returns to below growth rates. Perhaps well below.

Of course, if the main hidden tax in history has been pilfering by dynasty managers, that can result in a world where such pilferers spend a large fraction of world income, without much social value to show for it. One might easily dislike such a scenario, and want to prevent it. But instead of adding more explicit taxes to prevent the growth of dynastic funds, it seems to me better to cut the pilfering tax. Because this should encourage much more investment overall, which seems a good thing. This includes investment in helping and protecting the future, including protection from disasters, including existential risks. Which also seem like good things.

I’d like to try to make a point here that I’vemadebefore, but hopefully make it more clearly this time. My point is: trend tracking and policy analysis have little relevance for each other.

You can discuss education policy, or you can discuss education trends. You can discuss medical policy or you can discuss medical trends. You can discuss immigration policy, or you can discuss immigration trends. And you can discuss redistribution and inequality trends, or you can discuss redistribution and inequality policy. But in all of these cases, and many more, the trend and policy topics have little relevance for each other.

On trends, we collect a lot of data, usually on parameters that are relatively close to what we can easily measure, and also close to summary outcomes that we care about, like income, mortality, or employment. Many are interested in explaining past trends, and in forecasting future trends. Such trend tracking supports the familiar human need for news to discuss and fret about. And when a trend looks worrisome, that naturally leads people to want to discuss what oh what we might do about it.

On policy, we have lots of thoughtful theoretical analysis of policies, which try to judge which policies are better. And we have lots of relevant data analysis, that tries to distinguish relevant theories. Such analysis usually ends up identifying a few key parameters on which policy decisions should depend. But those tend to be abstract parameters, close to theoretical fundamentals. They usually have only a distant relation to the parameters which are tracked so eagerly as trends.

To repeat for emphasis: the easy to measure parameters where trends are most eagerly tracked are rarely close to the key theoretical parameters that determine which policies are best. They are in fact usually so far away that it is hard to judge the sign of the relation between them. This makes it unlikely that a change in one of these policies is a reasonable response to noticing some tracked-parameter trend.

For example which policies are best in medicine depends on key theoretical parameters like risk-aversion, asymmetric info on risks, meddling preferences, market power of hospitals, customer irrationality, and where learning happens, etc. But the trends we usually track are things like mortality, rates of new drug introduction, and amounts, fractions, and variance of spending. These later parameters are just not very relevant for inferring the former. People may find it fascinating to track trends in doctor salaries, cancer deaths, or how many are signed up for Obamacare. But those are pretty irrelevant to which policies are best.

As another example, debates on immigration refer to many relevant theoretical parameters, including meddling preferences, demand elasticity for low wage workers, and the intelligence, cultural norms, and cultural plasticity of immigrants. In contrast, trend trackers talk about trends in immigration, low-skill wages, wage inequality, labor share of income, voter participation, etc. Which might be fascinating topics, but they are just not very relevant for whether immigration is a good or bad idea. So it just doesn’t make sense to suggest changing immigration policy in response to noticing particular trends in these tracked parameters.

Alas, most people are a lot more interested in tracking trends than in analyzing policies. So well meaning people with smart things to say about policy often try to make their points seem more newsworthy by suggesting those policies as answers to the problems posed by troublesome trends. But, in doing so they usually mislead their audiences, and often themselves. Trends just aren’t very relevant for policy. If you want to talk policy, talk policy, and skip the trends.